Data stream classifiers including Mondrian Forests,
Micro-Cluster Nearest Neighbours [1]}, Hoeffding Trees [2]}, and simple Feedforward Neural
Networks [3]} are all commonly used in online contexts and were previously compared
in [4]} in the context of human activity recognition. Results showed superior performance of
Mondrian Forests, and Hoeffding Trees. However, both methods remain too memory intensive for connected objects where memory is typically in the order of \(\mathcal {O}\) (100KB) [5]}. Without compromising the breadth or depth of constructed forests, typically optimized hyperparameters which have a considerable impact on model performance, reducing the numerical precision used would potentially enable their application on memory-constrained platforms. However, given the inter-dependence of these parameters on model performance, numerical precision, memory consumption,
and model parameterization must be studied together. This paper uniquely quantifies the impact of reducing numerical precision on the performance of Mondrian Forests classifiers for activity recognition and evaluates the impact these results have on their portability.
